AI Data Engineer focused on Computational Toxicology at Pfizer. The role involves applying Data Engineering and MLOps best practices to automate scientific workflows, curate AI-ready datasets, and enable scalable AI solutions for drug safety science. This includes preparing, structuring, and validating data for AI-enabled toxicology assessments, integrating multi-modal datasets, and implementing scalable data pipelines.

This role focuses on designing, prototyping, and developing laboratory automation solutions for process development in healthcare, leveraging technologies like computer vision and machine learning. It involves integrating automated hardware, implementing robotic control and data management systems, and using coding languages like Python or C++ for AI/ML applications. The goal is to enhance automation for laboratory experimentation and drug development workflows.